On Identifying Global Nonlinear Discrete Models from Chaotic Data
From MaRDI portal
Publication:4399455
DOI10.1142/S0218127497001758zbMath0976.93501MaRDI QIDQ4399455
Eduardo M. A. M. Mendes, Stephen A. Billings
Publication date: 8 January 1999
Published in: International Journal of Bifurcation and Chaos (Search for Journal in Brave)
System identification (93B30) Nonlinear systems in control theory (93C10) Discrete-time control/observation systems (93C55) Strange attractors, chaotic dynamics of systems with hyperbolic behavior (37D45)
Related Items
The use of synthetic input sequences in time series modeling ⋮ On the use of interval extensions to estimate the largest Lyapunov exponent from chaotic data ⋮ On Overparametrization of Nonlinear Discrete Systems ⋮ Identification and reconstruction of chaotic systems using multiresolution wavelet decompositions ⋮ Modeling nonlinear dynamics and chaos: a review
Cites Work
- Structure identification of nonlinear dynamic systems - A survey on input/output approaches
- Input-output parametric models for non-linear systems Part I: deterministic non-linear systems
- Input-output parametric models for non-linear systems Part II: stochastic non-linear systems
- Orthogonal parameter estimation algorithm for non-linear stochastic systems
- Identification of non-linear output-affine systems using an orthogonal least-squares algorithm
- Representations of non-linear systems: the NARMAX model
- Application of a general multi-model approach for identification of highly nonlinear processes-a case study
- Orthogonal least squares methods and their application to non-linear system identification
- DISCRETE RECONSTRUCTION OF STRANGE ATTRACTORS OF CHUA’S CIRCUIT
- HIGHER-ORDER SPECTRAL ANALYSIS TO DETECT NONLINEAR INTERACTIONS IN MEASURED TIME SERIES AND AN APPLICATION TO CHUA’S CIRCUIT
- ON PERIODIC ORBITS AND HOMOCLINIC BIFURCATIONS IN CHUA’S CIRCUIT WITH A SMOOTH NONLINEARITY
- GLOBAL NONLINEAR POLYNOMIAL MODELS: STRUCTURE, TERM CLUSTERS AND FIXED POINTS
- Improved structure selection for nonlinear models based on term clustering
